context engineering is about legible intent

everyone talks about memory systems, retrieval pipelines, orchestration layers, token optimization, multi-agent workflows. all these frameworks. some of it's useful. but after a year of actually working with with ai agents -- shipping real client projects, building my own products, working daily in tools like cursor -- i think we're overcomplicating something fundamental. a huge part of context engineering isn't about infrastructure at all. it's about taste. not taste like some gatekeeping aesthetic thing. i mean taste as in knowing the difference between good and bad. knowing what you actually want. knowing what should exist and what shouldn't. knowing what to reject before it wastes your time. that judgment eventually shapes everything: how you write your prompts how you structure your project what examples you keep how you name things the feedback loops you build which information you retrieve your standards seep into the context itself. once that happens, what the model produces…